Automated system and method of retaining images based on a user&#39;s feedback on image quality

ABSTRACT

An automated system and method for retaining images in a smart phone are disclosed. The system may then determine a no-reference quality score of the image using a PIQUE module. The PIQUE module utilizes block level features of the image to determine the no-reference quality score. The system may present the image and the no-reference quality score to the user and accept a feedback towards quality of the image. The system may utilize a supervised learning model for continually learning a user&#39;s perception of quality of the image, the no-reference quality score determined by the PIQUE module, and the user feedback. Based on the learning, the supervised learning model may adapt the no-reference quality score and successively the image may either be retained or isolated for deletion, based on the adapted quality score and a predefined threshold range.

FIELD OF THE DISCLOSURE

The presently disclosed embodiments are generally related to power imagestorage based on quality of the image and more particularly to imagestorage based on a user's perception of image quality.

BACKGROUND

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of its mention in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also correspond toimplementations of the claimed technology.

Today's smart phone includes a high resolution camera having a fastimage capturing capability. Thus, it is easy to capture a large numberof images within seconds. Many of the smart phones provide a burst modefor capturing multiple images consecutively at a fast rate. While alarge number of images of a scene are captured quickly, a few or many ofthose images may not get captured properly. Some images may comprisenoise and the object of interest may be blurred.

It requires a lot a computation to process these images for separatingimages of good quality from images of poor quality. Such computationbecomes complex especially when a large number of images need to beprocessed. The smart phones also provide automated best shot selectionfrom the burst of images. The automated best shot selection may dependon factors such as lighting conditions, contrast, image resolution,dynamic range, and colour rendering.

The automated best shot selection may work only on images of burst shotsand not on images captured generally. Further, the images of goodquality, identified by the smart phone, may not be adequate from auser's perspective of image quality. Thus, it is important to understanda user's opinion of image quality for segregating all the images storedon his smart phone.

BRIEF SUMMARY

It will be understood that this disclosure is not limited to theparticular systems, and methodologies described, as there can bemultiple possible embodiments of the present disclosure which are notexpressly illustrated in the present disclosure. It is also to beunderstood that the terminology used in the description is for thepurpose of describing the particular versions or embodiments only, andis not intended to limit the scope of the present disclosure.

In an example embodiment, a method of retaining images based on a user'sfeedback on image quality is described. The method comprises receivingan image from either a camera or an archive of images. The methodfurther comprises of determining a no-reference quality score of theimage based on weights of blocks of the image. The weights of the blocksmay be determined based on features of the image. The method furthercomprises presenting at least one of the image, the no-reference qualityscore, and a spatial quality map of the image, to the user. The methodfurther comprises accepting a user feedback towards validation of theno-reference quality score of the image. The method further comprisescontinually learning user's perception of quality of the image based onprocessing of the image, the no-reference quality score determined bythe PIQUE module, and the user feedback. The method further comprisesretaining the image while the no-reference quality score of the imagelies between a predefined threshold range, else isolating the image fordeletion.

In an example embodiment, a system for retaining images based on auser's feedback on image quality is described. The system comprises aprocessor to receive images from either a camera or an archive ofimages. The system may further determine a no-reference quality score ofthe image based on weights of blocks of the image. The weights of theblocks may be determined based on features of the image. The system mayfurther present at least one of the image, the no-reference qualityscore, and a spatial quality map of the image, to the user. The systemmay further accept a user feedback towards validation of theno-reference quality score of the image. The system may furthercontinually learn a user's perception of quality of the image based onprocessing of the image, the no-reference quality score determined bythe PIQUE module, and the user feedback. The system may further retainthe image while the no-reference quality score of the image lies betweena predefined threshold range, else may isolate the image for deletion.

In an example embodiment, a non-transitory computer readable mediumembodying a program executable in a computing device for retainingimages based on a user's feedback on image quality is described. Theprogram may comprise a program code for receiving an image from one of acamera and an archive of images. The program may further comprise aprogram code for determining a no-reference quality score of the imagebased on weights of blocks of the image. The weights of the blocks maybe determined based on features of the image. The program may furthercomprise a program code for presenting at least one of the image, theno-reference quality score, and a spatial quality map of the image, tothe user. The program may further comprise a program code for acceptinga user feedback towards validation of the no-reference quality score ofthe image. The program may further comprise a program code forcontinually learning user's perception of quality of the image based onprocessing of the image, the no-reference quality score determined bythe PIQUE module, and the user feedback. The program may furthercomprise a program code for retaining the image while the no-referencequality score of the image lies between a predefined threshold range,else isolating the image for deletion.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments of systems,devices, methods, and embodiments of various other aspects of thedisclosure. Any person with ordinary skills in the art will appreciatethat the illustrated element boundaries (e.g. boxes, groups of boxes, orother shapes) in the figures represent one example of the boundaries. Itmay be that in some examples one element may be designed as multipleelements or that multiple elements may be designed as one element. Insome examples, an element shown as an internal component of one elementmay be implemented as an external component in another, and vice versa.Furthermore, elements may not be drawn to scale. Non-limiting andnon-exhaustive descriptions are described with reference to thefollowing drawings. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating principles.

FIG. 1 illustrates a network connection diagram 100 of a system 102 forretaining images based on a user's feedback on image quality, inaccordance with an embodiment of present disclosure.

FIG. 2 illustrates a methodology for block level distortionclassification and quantification for estimating a block score.

FIG. 3a illustrates segment pattern of a block of an image, used fordetermining the noise content present in the image, according to anexample embodiment.

FIG. 3b illustrates the segment pattern of a block of an image, used fordetermining noticeable distortions like blockiness and smoothnesscontent present in the image, according to an example embodiment.

FIG. 4a illustrates an image processed by the system 102, according toan example embodiment.

FIG. 4b illustrates a spatial quality map generated by the system 102for the image shown in FIG. 4a , according to an example embodiment.

FIG. 5 illustrates a user interface 500 of the system 102 for acceptinga user feedback towards quality of an image, according to an exampleembodiment.

FIG. 6 illustrates a flow chart showing a method 600 of retaining imagesbased on a user's feedback on image quality.

DETAILED DESCRIPTION

Some embodiments of this disclosure, illustrating all its features, willnow be discussed in detail. The words “comprising,” “having,”“containing,” and “including,” and other forms thereof, are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items.

It must also be noted that as used herein and in the appended claims,the singular forms “a,” “an,” and “the” include plural references unlessthe context clearly dictates otherwise. Although any systems and methodssimilar or equivalent to those described herein can be used in thepractice or testing of embodiments of the present disclosure, thepreferred, systems and methods are now described.

Unless specifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout this specificationdiscussions utilizing terms such as “receiving,” “determining,”“presenting,” “learning,” “retaining,” or the like refer to the actionsor processes that may be performed by a computing platform, such as acomputer or a similar electronic computing device, that is operable tomanipulate or transform data represented as physical, electronic ormagnetic quantities or other physical quantities within the computingplatform's processors, memories, registers, or other informationstorage, transmission, reception or display devices. Accordingly, acomputing platform refers to a system or a device that includes theability to process or store data in the form of signals. Thus, acomputing platform, in this context, may comprise hardware, software,firmware or any combination thereof. Further, unless specifically statedotherwise, a process as described herein, with reference to flowchartsor otherwise, may also be executed or controlled, in whole or in part,by a computing platform.

Embodiments of the present disclosure will be described more fullyhereinafter with reference to the accompanying drawings in which likenumerals represent like elements throughout the several figures, and inwhich example embodiments are shown. Embodiments of the claims may,however, be embodied in many different forms and should not be construedas limited to the embodiments set forth herein. The examples set forthherein are non-limiting examples and are merely examples among otherpossible examples.

It is an object of the current disclosure to provide a system, methodand device that enables retention of images based on a user's feedbackon image quality and user's perceptual preferences.

FIG. 1 illustrates a network connection diagram 100 of a system 102 forstoring images based on a user's feedback on image quality, inaccordance with an embodiment of present disclosure. The system 102 isgenerally implemented on a smart phone as illustrated in FIG. 1, butcould also be implemented on a remote server 106 of FIG. 1. The smartphone 102 may be connected to the remote server 106 through acommunication network 104. The communication network 104 connecting thesmart phone and the remote server 106 may be essentially implementedusing IEEE 802.11 standard. Further, the communication network may useother communication techniques for achieving connectivity betweendifferent units or modules. The other communication techniques may beselected from Visible Light Communication (VLC), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution(LTE), Wireless Local Area Network (WLAN), Infrared (IR) communication,Public Switched Telephone Network (PSTN), and Radio waves.

The system 102 comprises a camera 108 and a processor 110. The processor110 may execute computer program instructions stored in a memory 114.The memory 114 may refer to an internal memory of the system 102. Theprocessor 110 may also be configured to decode and execute anyinstructions received from one or more other electronic devices or oneor more remote servers. The processor 110 may include one or moregeneral purpose processors (e.g., ARM®, INTEL®, and NVIDIA®microprocessors) and/or one or more special purpose processors (e.g.,digital signal processors or Xilinx System On Chip (SOC) FieldProgrammable Gate Array (FPGA) processor). The processor 110 may beconfigured to execute one or more computer-readable programinstructions, such as program instructions to carry out any of thefunctions described in this description.

Interface(s) 112 may be used to interact with or program the system 102to achieve desired functionality. The interface(s) 112 may either beCommand Line Interface (CLI) or Graphical User Interface (GUI). Theinterface(s) 112 may also be used for accepting user feedbacks andinputs.

The memory 114 may include a computer readable medium. A computerreadable medium may include volatile and/or non-volatile storagecomponents, such as optical, magnetic, organic or other memory or discstorage, which may be integrated in whole or in part with a processor,such as processor 110. Alternatively, the entire computer readablemedium may be remote from processor 110 and coupled to processor 110 byconnection mechanism and/or network cable. In addition to memory 114,there may be additional memories that may be coupled with the processor110. The memory 114 may comprise a Perception based Image QualityEvaluator (PIQUE) module 116, a supervised learning model 118, and animage retaining module 120.

In one embodiment, the processor 110 may receive an image. In one case,the image may be received from the camera 108 of the smart phone 102using a frame grabber. By the frame grabber, a frame capture request maybe sent to Hardware Abstraction Layer (HAL) of the camera 108.Successively, an image captured by the camera 108 may be received in abuffer and may be delivered to the processor 110 at a fixed frame rate.Alternatively, the image may be retrieved from the memory 114 of thesmart phone 102. Further, the processor 114 may receive multiple imagesfor parallel processing, from the memory 114 of the smart phone 102.

In one embodiment, the images may be stored in an external memory 122connected to the smart phone 102. The external memory 122 may be presentas a Secure Digital (SD) card, or micro SD card, or Universal Serial Bus(USB) flash drive. The external memory 122 may either be volatile ornon-volatile in nature.

In one embodiment, the images may be stored on a server 106. The imagescaptured by the smart phone 102 may continuously be updated ortransferred to the server 102. The server 102 may indicate a cloudstorage facility offered by an Internet Service Provider (ISP) or atelecommunication service provider. For example, the images captured onan i-Phone™ are stored over iCloud™ network.

Successively, the image may be transferred to the Perception based ImageQuality Evaluator (PIQUE) module 116 for further processing. The PIQUEmodule 116 may function to determine a no-reference quality score of theimage. In order to determine the no-reference quality score, the PIQUEmodule 116 may pre-process the image, at a first stage. The image may bepresent in different colour formats such as RGB, YUV, and YIQ. The imagemay be pre-processed for colour conversion. The image may be convertedfrom an existing colour format into a gray scale image.

At a second stage, during pre-processing, the image may be divisivelynormalized. Divisive normalization of the image helps in accuratecapturing of edge information of the image. In one case, transformedluminance values may be identified as Mean Subtracted ContrastNormalized (MSCN) coefficients for the image I(i, j) using a belowmentioned equation 1.

$\begin{matrix}{{\hat{I}\left( {i,j} \right)} = \frac{{I\left( {i,j} \right)} - {\mu\left( {i,j} \right)}}{{V\left( {i,j} \right)} + C}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In above equation 1, i and j are spatial indices and i€1, 2 . . . M andj€1, 2 . . . N. M and N are image height and width respectively. μ(i,j)denotes local mean in the image. V(i,j) denotes variance in the image.‘C’ is a constant set to a pre-defined non-zero value to preventinstability of the MSCN coefficient values. For example, in one case,the constant ‘C’ is not used and details of an image of clear sky areprocessed to determine an MSCN coefficient value. All the pixels of suchimage may have uniform values and a value of the variance may be ‘0.’The denominator may have a value of ‘0’ in such case. As the denominatorhas the value ‘0,’ the MSCN coefficient value may become unstable. Toprevent such instability, the constant ‘C’ is used. In one case, ‘C’ isset as 1.

In above mentioned equation 1,

$\begin{matrix}{{\mu\left( {i,j} \right)} = {\sum\limits_{k = {- K}}^{K}\;{\sum\limits_{l = {- L}}^{L}\;{w_{k,l}{I_{k,l}\left( {i,j} \right)}}}}} & {{Equation}\mspace{14mu} 2} \\{{V\left( {i,j} \right)} = \sqrt{\sum\limits_{k = {- K}}^{K}\;{\sum\limits_{l = {- L}}^{L}\;{w_{k,l}\left( {{I_{k,l}\left( {i,j} \right)} - {\mu\left( {i,j} \right)}} \right)}^{2}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In above mentioned equations 2 and 3, w={w_(k, 1)|k=−K, . . . K, 1=−L, .. . L} is a 2D-circularly symmetric Gaussian weighting function sampledout to 3 standard deviations (K=L=3) and rescaled to a unit volume.Thus, based on the above described processing, a divisive normalizedimage is obtained. In the divisive normalized image, pixels are meansubtracted and contrast is normalized.

At a second stage in the PIQUE module 116, feature extraction and blockclassification of the divisive normalized image may be performed todetermine feature vector for each block of the image, where a blockrepresents an array of pixels in the image. The image may be scanned ina block-wise manner i.e. scanning block 1 at first and subsequentlyscanning block 2, block 3, and other blocks in a sequence. In one case,the block size may be (n×n) with n=16, and ‘n’ denotes a number ofpixels in the image. Features for each block of the image may beextracted. The features may comprise derivatives of standard deviationof edge segments of divisively normalized images. Based on the extractedfeatures, each block may be classified as either a distorted block or anundistorted block.

In one embodiment, during block classification, the MSCN coefficients ofthe image may be utilized to label a block either as a uniform (U) blockor a spatially active block. Further, spatially active blocks areanalysed for two types of distortion criteria, namely NoticeableDistortion Criterion (NDC) and Additive White Noise Criterion (NC).Detailed explanation related to these criteria is provided henceforth.Once a given block is determined to be a distorted block, weight issubsequently assigned to the distorted block, depending on the amount ofdistortion contributed by that block to the overall score. Derivation ofthese weights is explained henceforth with reference to FIG. 2. FIG. 2shows a methodology for block level distortion classification andquantification for estimating a block score.

In one embodiment, during feature extraction and block classification,the divisive normalized image may be segmented into non-overlappingblocks. In one case, the divisive normalized image may be segmented intoblocks B_(k) of size ‘n×n.’ In one case, blocks present at the divisivenormalized image boundaries on all four sides may be discarded. The MSCNcoefficients of the image may be utilized to label a block either as auniform (U) block or a non-uniform block. The non-uniform block may alsobe identified as a spatially active (SA) block. The criterion forlabelling the blocks is as provided below using equation 4.

$\begin{matrix}{B_{k} = \left\{ \begin{matrix}{U,{v_{k} < T_{U}}} \\{{SA},{v_{k.} \geq T_{U}}}\end{matrix} \right.} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In above mentioned equation 4, ν_(k) is variance of the coefficients,Î(i,j) in a given block B_(k) with kϵ1, 2, . . . NB. NB defines a totalnumber of blocks of size ‘n×n.’ In one implementation, n=16 may be used.T_(U) denotes a predetermined threshold, and based on empiricalobservations, value of T_(U) may be set as 0.1, in one case.

The identified spatially active blocks, B_(k) may be analysed forpresence of a Noticeable Distortion Criterion (NDC) and an additivewhite Noise Criterion (NC). A distorted block may then be assigned adistortion score based on the type of distortion present in thedistorted block. A block level distortion may be noticeable while atleast one of the edge segment exhibits a low spatial activity(Noticeable Distortion Criterion, NDC). A segment may be defined as acollection of 6 contiguous pixels in a block edge as shown in FIG. 3b .For a spatially active block B_(k) derived from Î(i,j) of size ‘n×n’with n=16, each edge ‘L_(p)’ may be divided into eleven segments asmentioned below, in equation 5.a _(pq) =L _(p)(x):x=q,q+1,q+2, . . . q+5,  Equation 5

In above mentioned equation 5, a_(pq) denotes the structuring element,p€1, 2, 3, 4 denotes edge index, and q€1, 2, 3, . . . 11 denotes thesegment index.

A segment may exhibit low spatial activity while a standard deviation(σ_(pq)) of the segment remains lesser than a threshold T_(STD), asmentioned below in equation 5.σ_(pq) <T _(STD)  Equation 6

To avoid difficulty in threshold determination while using luminanceimages, MSCN coefficients may be used in an embodiment. Usage of theMSCN coefficients may help in significantly reducing thresholdvariability. The empirical threshold for T_(STD) may be set as 0.1, inone case. Successively, a block may be considered as distorted while atleast one of its segments satisfies the above mentioned equation 6. Thefollowing sub-section explains the criterion used to classify andestimate noise in a given block.

In one embodiment, perception based center-surround criterion may beconsidered to model noise distortion using block level MSCN features.Such criterion may be related to Human Visual System's (HVS) sensitivityto center-surround variations. A block may be divided into two segments,as shown in FIG. 3a . The two segments may comprise a central segmentcomprising two center columns (S_(cen)) and a surrounding segment(S_(sur)) comprising remaining columns.

In one embodiment, a relation between center-surround deviation ratioand the block standard deviation of an MSCN block may be modelled usingparameter ‘β’ defined using below mentioned equation 7.

$\begin{matrix}{\beta = \frac{{\left( {\sigma_{cen}/\sigma_{sur}} \right) - \sigma_{blk}}}{\max\left( {\left( {\sigma_{cen}/\sigma_{sur}} \right),\sigma_{blk}} \right)}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

In above mentioned equation 7, σ_(cen) denotes the standard deviation ofa segment S_(cen), σ_(sur) is the standard deviation of segment S_(sur),and σ_(blk) is the standard deviation of spatially active block B_(k)derived from Î(i,j). Thus, a block B_(k) may be identified as beingaffected with noise while it satisfies the condition (Noise Criterion,NC) provided in below mentioned equation 8.σ_(blk)>2*β  Equation 8

At a third stage of pre-processing, the computed scores may be pooled.For score pooling, an amount of distortion contributed by a distortedblock to an overall score may be determined. In one case, variance(ν_(blk)) of the MSCN coefficients of a given block may representsignificant signature of an amount of distortion present in that block.A distortion assignment procedure may be defined for a given blockB_(k), using a below mentioned equation 9.

$\begin{matrix}{D_{sk} = \left\{ \begin{matrix}{{{1\mspace{14mu}{if}\mspace{14mu}(6)}\&}\mspace{11mu}(8)} \\{v_{blk}\mspace{14mu}{if}\mspace{14mu}(8)} \\{\left( {1 - v_{blk}} \right)\mspace{14mu}{if}\mspace{14mu}(6)}\end{matrix} \right.} & {{Equation}\mspace{14mu} 9}\end{matrix}$

In above mentioned equation 9, D_(sk) is distortion assigned to a block.Finally, the PIQUE module 116 pools the weights of distorted blocks todetermine a no-reference quality score for the image based on a belowmentioned equation 10.

$\begin{matrix}{{PIQUE} = \left\lbrack {\left( {\sum\limits_{k = 0}^{N_{SA}}\;\left( D_{sk} \right)} \right) + {C\;{1/\left( {{C\; 1} + N_{SA}} \right)}}} \right\rbrack} & {{Equation}\mspace{14mu} 10}\end{matrix}$

In above mentioned equation 10, ‘N_(SA)’ denotes the number of spatiallyactive blocks in the image. Value of D_(sk) may not exceed 1 in anycase, and thus the PIQUE score i.e. the no-reference quality score mayrange between 0 and 1. ‘C1’ indicates a pre-defined constant havinganon-zero value. ‘C1’ may prevent instability of the PIQUE score whilethe denominator (N_(SA)) has a ‘0’ value.

In one embodiment, the PIQUE module 116 may generate a spatial qualitymap for the image. The spatial quality map may help a user inidentifying noise or distortion present in each block of the image. FIG.4b illustrates a spatial quality map of a JP2K (JPEG 2000) DistortedImage from LIVE Database with added Additive White Gaussian Noise (AWGN)noise patches, as illustrated in FIG. 4a . In one case, differentcolours of the blocks may be used for identifying the blockscorresponding to Noticeable Distortion Criterion (NDC), Noise Criterion(NC), and uniform block (U) criterions. In another case, as shown inFIG. 4b , the block having solid line borders represent NC and theblocks having dotted line borders represent NDC.

The no-reference quality score generated by the PIQUE module 116 may beprovided to the image retaining module 120. The image retaining module120 may retain the image while the no-reference quality score of theimage lies between a predefined threshold range, else the image may beisolated for deletion. In one case, the predefined threshold range of0-0.3 may indicate a good quality of the image and may result inretention of the image. No-reference quality score for the image,ranging from 0.3-0.5 in one case, may indicate an average quality of theimage. Such image of average quality may be retained in the memory,isolated into a different memory location for deletion, or deleted basedon a user input. No-reference quality score for the image, ranging from0.5-1 in one case, may indicate poor quality of the image. Such image ofpoor quality may either be isolated into a different memory location fordeletion or may be deleted based on set user preferences.

In one embodiment, the system may be provided with the learningcapability by integrating the system 102 with a supervised learningmodel 118. The supervised learning 118 model may be configured tocontinuously learn user's perception of quality of the image. To learnthe user's perception of quality of the image, the supervised learningmodel 118 may receive a user feedback towards quality of an image.Successively, the supervised learning model 118 may process the image,the no-reference quality score determined by the PIQUE module 116, and auser feedback towards quality of the image.

In one embodiment, the user feedback may be a score ranging from 1 to 5.The score may be provided to the system 102 as a star rating using theinterface(s) 112, as illustrated in FIG. 5. Further, the user may referto the spatial quality map of the image to provide the user feedback onquality of the image.

In another embodiment, the supervised learning model 118 mayincrementally learn the user's perceptual preferences towards quality ofthe images, based on training data. The training data may compriseuser's feedbacks towards quality of a plurality of images presented tothe user, over a period of time. The user may be asked for userfeedbacks on image quality of recently captured images or images storedin the memory 114 of the smart phone 102.

In one embodiment, the user feedback may be accepted while the smartphone 102 enters into an active mode from an idle mode i.e. the userunlocks the smart phone 102. The supervised learning model 118 may thuscontinually learn from the user's feedbacks on the quality of imagespresent in the system and may affect the no-reference quality scorebased on the user's feedbacks. Successively, the image retaining module120 may retain the images based on the new no-reference quality scoresgenerated for the images, based on the user's feedback on the quality ofimages.

In one embodiment, the supervised learning model 118 may comprise aperiodic recalibration engine. The periodic recalibration engine maymonitor deviations from the user's perception of image quality anderrors introduced during the continuous learning process. This periodicrecalibration engine may work as a self correction mechanism during thelearning process. In case of identification of the deviation between theno-reference quality score and the user's perception of image quality,the periodic recalibration engine may start accepting user feedbacks fortuning the no-reference score as per the user's perception of imagequality.

The test results showing performance of the PIQUE module 116 ondifferent publicly available databases having human subjective scoresare as provided below. Correlation scores Spearman Rank OrderCorrelation Coefficient (SROCC) and Pearson Correlation Coefficient(PCC) of the PIQUE module generated on LIVE database are as mentionedbelow.

Correlation score JP2K JPEG WN GBLUR ALL SROCC 0.93 0.89 0.96 0.92 0.91PCC 0.93 0.90 0.94 0.90 0.90

Correlation scores SROCC and PCC of the PIQUE module generated on CSIQdatabase are as mentioned below.

Correlation score JP2K JPEG WN GBLUR ALL SROCC 0.87 0.86 0.91 0.86 0.84PCC 0.90 0.88 0.93 0.88 0.87

Correlation scores SROCC and PCC of the PIQUE module generated on TIDdatabase are as mentioned below.

Correlation score JP2K JPEG WN GBLUR ALL SROCC 0.93 0.83 0.78 0.85 0.85PCC 0.93 0.87 0.78 0.84 0.86

FIG. 6 illustrates a flowchart of a method of retaining images based ona user's feedback on image quality, according to an embodiment. FIG. 6comprises a flowchart 600 that is explained in conjunction with theelements disclosed in FIG. 1.

The flow chart of FIG. 6 show the method steps executed according to oneor more embodiments of the present disclosure. In this regard, eachblock may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that in somealternative implementations, the functions noted in the blocks may occurout of the order noted in the drawings. For example, two blocks shown insuccession in FIG. 6 may in fact be executed substantially concurrentlyor the blocks may sometimes be executed in the reverse order, dependingupon the functionality involved. Any process descriptions or blocks inflow charts should be understood as representing modules, segments, orportions of code which include one or more executable instructions forimplementing specific logical functions or steps in the process, andalternate implementations are included within the scope of the exampleembodiments in which functions may be executed out of order from thatshown or discussed, including substantially concurrently or in reverseorder, depending on the functionality involved. In addition, the processdescriptions or blocks in flow charts should be understood asrepresenting decisions made by a hardware structure such as a statemachine. The flowchart 600 starts at the step 602 and proceeds to step614.

At step 602, an image is received from one of a camera or an archive ofimages. In one embodiment, the image may be received by a processor 110.

At step 604, a no-reference quality score of the image may bedetermined. In one embodiment, the no-reference quality score of theimage may be determined by a Perception based Image Quality Evaluator(PIQUE) module 116.

At step 606, a user feedback towards quality of the image may beaccepted. In one embodiment, the user feedback may be accepted by theprocessor 110. In one case, the user feedback may be accepted while thesmart phone 102 is unlocked by the user. Further, the user feedback maybe accepted in different ways and during different instances.

At step 608, a user's perception of a quality of image may be learnt. Inone embodiment, the user's perception of quality of image may be learntby a supervised learning model 118.

At step 610, the no-reference quality score may be checked to be presentwithin a predefined threshold range. In one embodiment, the no-referencequality score may be checked by the processor 110.

At step 612, the image may be retained in the memory while theno-reference quality score is present within the predefined thresholdrange. In one embodiment, the image may be retained by an imageretaining module 120.

At step 614, the image may be isolated for deletion while theno-reference quality score of the image is not present within thepredefined threshold range. In one embodiment, the image may be isolatedfor deletion by the image retaining module 120.

The logic of the example embodiment(s) can be implemented in hardware,software, firmware, or a combination thereof. In example embodiments,the logic is implemented in software or firmware that is stored in amemory and that is executed by a suitable instruction execution system.If implemented in hardware, as in an alternative embodiment, the logiccan be implemented with any or a combination of the followingtechnologies, which are all well known in the art: a discrete logiccircuit(s) having logic gates for implementing logic functions upon datasignals, an application specific integrated circuit (ASIC) havingappropriate combinational logic gates, a programmable gate array(s)(PGA), a field programmable gate array (FPGA), etc. In addition, thescope of the present disclosure includes embodying the functionality ofthe example embodiments disclosed herein in logic embodied in hardwareor software-configured mediums.

Embodiments of the present disclosure may be provided as a computerprogram product, which may include a computer-readable medium tangiblyembodying thereon instructions, which may be used to program a computer(or other electronic devices) to perform a process. Thecomputer-readable medium may include, but is not limited to, fixed(hard) drives, magnetic tape, floppy diskettes, optical disks, CompactDisc Read-Only Memories (CD-ROMs), and magneto-optical disks,semiconductor memories, such as ROMs, Random Access Memories (RAMs),Programmable Read-Only Memories (PROMs), Erasable PROMs (EPROMs),Electrically Erasable PROMs (EEPROMs), flash memory, magnetic or opticalcards, or other type of media/machine-readable medium suitable forstoring electronic instructions (e.g., computer programming code, suchas software or firmware). Moreover, embodiments of the presentdisclosure may also be downloaded as one or more computer programproducts, wherein the program may be transferred from a remote computerto a requesting computer by way of data signals embodied in a carrierwave or other propagation medium via a communication link (e.g., a modemor network connection).

Moreover, although the present disclosure and its advantages have beendescribed in detail, it should be understood that various changes,substitutions and alterations can be made herein without departing fromthe disclosure as defined by the appended claims. Moreover, the scope ofthe present application is not intended to be limited to the particularembodiments of the process, machine, manufacture, composition of matter,means, methods and steps described in the specification. As one willreadily appreciate from the disclosure, processes, machines,manufacture, compositions of matter, means, methods, or steps, presentlyexisting or later to be developed that perform substantially the samefunction or achieve substantially the same result as the correspondingembodiments described herein may be utilized. Accordingly, the appendedclaims are intended to include within their scope such processes,machines, manufacture, compositions of matter, means, methods, or steps.

What is claimed is:
 1. An automated method of retaining images based ona user's feedback on image quality, the method comprising: receiving, bya processor, an image from one of a camera and an archive of images;determining, by the processor, a no-reference quality score of the imagebased on weights of blocks of the image, wherein the weights of theblocks are determined based on features of the image; presenting, by aninterface, at least one of the image, the no-reference quality score, ora spatial quality map of the image, to the user; receiving, by theprocessor, a user feedback towards validation of the no-referencequality score of the image; continually learning, by a learning model,user's perception of quality of the image based on processing of theimage, the no-reference quality score, and the user feedback; adjusting,by the processor, the no-reference quality score based on the user'sperception of the quality of the image; monitoring, by the processor,deviations from the user's perception of the quality of the image anderrors introduced during the continuous learning; and retaining, by theprocessor, the image while the no-reference quality score of the imagelies between a predefined threshold range, else isolating the image fornot to be retained.
 2. The method of claim 1, wherein the no-referencequality score is determined while the processor is present in an idlemode.
 3. The method of claim 1, wherein the no-reference quality scoreis determined by a Perception based Image Quality Evaluator (PIQUE)module using the relation,${{{Quality}\mspace{14mu}{Score}} = \left\lbrack {\left( {\sum\limits_{k = 0}^{N_{SA}}\;\left( D_{sk} \right)} \right) + {C\;{1/\left( {{C\; 1} + N_{SA}} \right)}}} \right\rbrack},$wherein D_(sk) is distortion assigned to a block of the image, N_(SA) isnumber of spatially active blocks of the image.
 4. The method of claim1, wherein the no-reference quality score ranges from 0 to
 1. 5. Themethod of claim 1, wherein the features comprise derivatives of standarddeviation of edge segments of divisively normalized images.
 6. Themethod of claim 1, wherein the user feedback is accepted while a systemused for retaining the image enters into an active mode from an idlemode.
 7. The method of claim 1, wherein the user feedback is a scoreranging from 1 to
 5. 8. The method of claim 1, wherein the learningmodel continually learns the user's perception of the quality of theimage based on training data generated from user's feedbacks towardsquality of a plurality of images comprising dissimilar data content. 9.The method of claim 1, wherein the spatial quality map illustrates atleast one of an amount and type of distortion present in the blocks ofthe image.
 10. A system for retaining images based on a user's feedbackon image quality, the system comprising: a processor; a camera; and amemory coupled to the processor, wherein the processor executes analgorithm stored in the memory to: receive, an image from one of thecamera and an archive of images; determine, using a Perception basedImage Quality Evaluator (PIQUE) module, a no-reference quality score ofthe image based on weights of blocks of the image, wherein the weightsof the blocks are determined based on features of the image; present,using an interface, at least one of the image, the no-reference qualityscore, and a spatial quality map of the image, to the user; accept,using the interface, a user feedback towards validation of theno-reference quality score of the image; continually learn, using alearning model, user's perception of quality of the image based onprocessing of the image, the no-reference quality score determined bythe PIQUE module, and the user feedback; adjust the no-reference qualityscore based on the user's perception of the quality of the image;monitor deviations from the user's perception of the quality of theimage and errors introduced during the continuous learning; and retain,using an image retaining module, the image while the no-referencequality score of the image lies between a predefined threshold range,else isolate the image for not to be retained.
 11. The system of claim10, wherein the no-reference quality score is determined while theprocessor is present in an idle mode.
 12. The system of claim 10,wherein the no-reference quality score is determined by the PIQUE moduleusing the relation,${{{Quality}\mspace{14mu}{Score}} = \left\lbrack {\left( {\sum\limits_{k = 0}^{N_{SA}}\;\left( D_{sk} \right)} \right) + {C\;{1/\left( {{C\; 1} + N_{SA}} \right)}}} \right\rbrack},$wherein D_(sk) is distortion assigned to a block of the image, N_(SA) isnumber of spatially active blocks of the image.
 13. The system of claim10, wherein the no-reference quality score ranges from 0 to
 1. 14. Thesystem of claim 10, wherein the features comprise derivatives ofstandard deviation of edge segments of divisively normalized images. 15.The system of claim 10, wherein the user feedback is accepted while thesystem enters into an active mode from an idle mode.
 16. The system ofclaim 10, wherein the user feedback is a score ranging from 1 to
 5. 17.The system of claim 10, wherein the learning model continually learnsthe user's perception of the quality of the image based on training datagenerated from user's feedbacks towards quality of a plurality of imagescomprising dissimilar data content.
 18. The system of claim 10, whereinthe spatial quality map illustrates at least one of an amount and typeof distortion or noise present in the blocks of the image.
 19. Anon-transient computer-readable medium comprising instructions forcausing a programmable processor to: receive an image from one of acamera and an archive of images; determine a no-reference quality scoreof the image based on weights of blocks of the image, wherein theweights of the blocks are determined based on features of the image;present to the user, at least one of the image, the no-reference qualityscore, and a spatial quality map of the image; accept a user feedbacktowards validation of the no-reference quality score of the image;continually learn user's perception of quality of the image based onprocessing of the image, the no-reference quality score determined by aPerception based Image Quality Evaluator (PIQUE) module, and the userfeedback; adjust the no-reference quality score based on the user'sperception of the quality of the image; monitor deviations from theuser's perception of the quality of the image and errors introducedduring the continuous learning; and retain the image while theno-reference quality score of the image lies between a predefinedthreshold range, else isolate the image for not to be retained.